{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "95505d4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simpleNote: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: scipy in d:\\anaconda\\lib\\site-packages (1.6.2)\n",
      "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in d:\\anaconda\\lib\\site-packages (from scipy) (1.20.1)\n",
      "Could not fetch URL https://pypi.tuna.tsinghua.edu.cn/simple/pip/: There was a problem confirming the ssl certificate: HTTPSConnectionPool(host='pypi.tuna.tsinghua.edu.cn', port=443): Max retries exceeded with url: /simple/pip/ (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:1125)'))) - skipping\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%pip install scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92c82477",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./csv/centrality analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1c52942c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='degree'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.degree'\n",
    "pd.DataFrame(df[label]).value_counts().plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48f9fe89",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "scatter requires an x and y column",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-ea72df2a76b0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'n.degree'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Degree\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mylabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Count\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"scatter\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlogx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    898\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataframe_kinds\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    899\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 900\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    901\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    902\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"plot kind {kind} can only be used for data frames\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     58\u001b[0m                 \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     61\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, x, y, s, c, **kwargs)\u001b[0m\n\u001b[0;32m   1003\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0ms\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1004\u001b[0m             \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1005\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1006\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1007\u001b[0m             \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, x, y, **kwargs)\u001b[0m\n\u001b[0;32m    926\u001b[0m         \u001b[0mMPLPlot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    927\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 928\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kind\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\" requires an x and y column\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    929\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    930\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: scatter requires an x and y column"
     ]
    }
   ],
   "source": [
    "label = 'n.degree'\n",
    "pd.DataFrame(pd.DataFrame(df[label]).value_counts().to_frame()).plot(xlabel=\"Degree\",ylabel=\"Count\",kind=\"scatter\",logx=True, logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6a167c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(MultiIndex([(   8.0,),\n",
       "             (  15.0,),\n",
       "             (   9.0,),\n",
       "             (   4.0,),\n",
       "             (   6.0,),\n",
       "             (   7.0,),\n",
       "             (   2.0,),\n",
       "             (  10.0,),\n",
       "             (   3.0,),\n",
       "             (   5.0,),\n",
       "             ...\n",
       "             ( 211.0,),\n",
       "             ( 210.0,),\n",
       "             ( 209.0,),\n",
       "             ( 204.0,),\n",
       "             ( 200.0,),\n",
       "             ( 111.0,),\n",
       "             ( 144.0,),\n",
       "             ( 149.0,),\n",
       "             ( 163.0,),\n",
       "             (1045.0,)],\n",
       "            names=['n.degree'], length=227),\n",
       " array([111, 106, 100,  99,  98,  98,  98,  95,  93,  93,  87,  82,  82,\n",
       "         81,  79,  76,  75,  73,  72,  63,  63,  60,  56,  55,  53,  52,\n",
       "         49,  44,  44,  43,  43,  43,  40,  38,  38,  37,  36,  35,  33,\n",
       "         29,  29,  29,  27,  25,  24,  24,  24,  23,  23,  23,  23,  22,\n",
       "         21,  21,  20,  20,  19,  19,  18,  18,  18,  18,  17,  17,  16,\n",
       "         16,  16,  15,  15,  15,  15,  14,  13,  13,  12,  12,  12,  11,\n",
       "         11,  11,  11,  11,  11,  10,  10,  10,  10,  10,  10,  10,   9,\n",
       "          9,   9,   9,   9,   9,   9,   9,   9,   9,   9,   8,   8,   8,\n",
       "          8,   7,   7,   7,   7,   7,   7,   7,   6,   6,   6,   6,   6,\n",
       "          6,   6,   6,   6,   6,   6,   6,   6,   5,   5,   5,   5,   5,\n",
       "          5,   5,   5,   5,   5,   5,   5,   5,   5,   5,   5,   4,   4,\n",
       "          4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,\n",
       "          4,   4,   4,   4,   4,   4,   4,   4,   4,   3,   3,   3,   3,\n",
       "          3,   3,   3,   3,   3,   3,   3,   3,   3,   3,   3,   3,   3,\n",
       "          2,   2,   2,   2,   2,   2,   2,   2,   2,   2,   2,   2,   2,\n",
       "          2,   2,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
       "          1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,   1,\n",
       "          1,   1,   1,   1,   1,   1], dtype=int64))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df[label]).value_counts().index,pd.DataFrame(df[label]).value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "75508a19",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='eigenvector'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.eigenvector'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e19e6477",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pagerank'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.pagerank'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c48d8d52",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='closeness'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.closeness'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7ad7b2fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='betweenness'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.betweenness'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "82fd63e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pregel_auth'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAELCAYAAADX3k30AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAie0lEQVR4nO3df3xU9Z3v8dcn4bcIVEChBAV7qS1opTalqNyubrdV1BZ726762NZVW388CrZur4+W6mqRbrurdXe7tS6IP7b12lbvRX2IStVtlVZAJKCU378EhABC+BV+JSHJfO4fcxImk5nMmUkmMzl5Px+PPDJzzvec85mTyXvOfM8vc3dERCS6SgpdgIiI5JeCXkQk4hT0IiIRp6AXEYk4Bb2ISMQp6EVEIi5U0JvZ5Wa2wcw2m9n0FOM/ZmZvmVmdmd2ZzbQiIpJfluk4ejMrBTYCnwcqgQrgOndfm9DmdOAs4GrgoLs/GHZaERHJrzBb9BOAze6+xd1PAE8DUxIbuPted68A6rOdVkRE8qtHiDYjgB0JzyuBz4Scf07TDhkyxEeNGhVyESIisnz58n3uPjTVuDBBbymGhb1uQuhpzewW4BaAM888k2XLloVchIiImNn76caF6bqpBEYmPC8DdoVcduhp3X2Ou5e7e/nQoSk/lEREJAdhgr4CGGNmo82sF3AtMC/k/NszrYiIdICMXTfu3mBm04BXgVLgCXdfY2a3BeNnm9kwYBkwAIiZ2R3AWHc/nGraPL0WERFJIePhlYVQXl7u6qMXKYz6+noqKyupra0tdCmSQp8+fSgrK6Nnz54thpvZcncvTzVNmJ2xItKNVFZWcuqppzJq1CjMUh1PIYXi7uzfv5/KykpGjx4dejpdAkFEWqitrWXw4MEK+SJkZgwePDjrb1sKehFpRSFfvHL52yjoJXL+vLGK+sZYocsQKRoKeomUpVsPcP0TS3nwtQ2FLkWkaCjoJVL2H60D4P19xwtciUTRDTfcwNy5cztsfitWrGD+/PnNz2fMmMGDDz7YYfNvoqAXkchqbGwsdAltSg76fNHhlSKS1n0vrmHtrsMdOs+xHx7Aj744rs0227ZtY/LkyUyaNInFixczYsQIXnjhBfr27dvcZsaMGbz33nvs3LmTHTt28P3vf5+bb76ZBQsWcN999zF8+HBWrFjBqlWrmD59OgsWLKCuro6pU6dy6623EovFmDZtGn/6058YPXo0sViMm266ia9+9assX76c733vexw9epQhQ4bwq1/9iuHDh2d8bTNnzuTFF1+kpqaGiy66iEceeQQz45JLLuHBBx+kvLycffv2UV5ezsaNG7n33nupqalh4cKF/PCHPwRg7dq1XHLJJWzfvp077riD73znO+1b4WiLXkSK1KZNm5g6dSpr1qxh0KBBPPvss63arFy5kpdffpm33nqLmTNnsmtX/FJaS5cu5Sc/+Qlr167l8ccfZ+DAgVRUVFBRUcGjjz7K1q1bee6559i2bRurVq3iscce46233gLiJ4zdfvvtzJ07l+XLl3PTTTdx9913h6p52rRpVFRUsHr1ampqanjppZfStu3VqxczZ87kmmuuYcWKFVxzzTUArF+/nldffZWlS5dy3333UV+ffPX37GmLXiLJQ19gVdqSacs7n0aPHs348eMB+NSnPsW2bdtatZkyZQp9+/alb9++XHrppSxdupRBgwYxYcKE5hOKXnvtNVauXNnct15dXc2mTZtYuHAhX/va1ygpKWHYsGFceumlAGzYsIHVq1fz+c9/Hoh3/4TZmgd44403eOCBBzh+/DgHDhxg3LhxfPGLX8zqdV955ZX07t2b3r17c/rpp7Nnzx7KysqymkcyBb1Eig7/jo7evXs3Py4tLaWmpqZVm+Rjypuen3LKKc3D3J2HHnqIyy67rEXbl19+OeVy3Z1x48Y1b+GHVVtby7e//W2WLVvGyJEjmTFjRvOJTT169CAWizW3a0vy625oaMiqjlTUdSMiXdYLL7xAbW0t+/fvZ8GCBXz6059u1eayyy5j1qxZzV0gGzdu5NixY0yaNIlnn32WWCzGnj17WLBgAQDnnHMOVVVVLbpy1qzJfC3GpgAfMmQIR48ebXF0zqhRo1i+fDlAi+GnnnoqR44cye3FZ0FBLyJdxuzZs5k9e3bz8wkTJnDllVcyceJE7rnnHj784Q+3muZb3/oWY8eO5YILLuDcc8/l1ltvpaGhga985SuUlZU1D/vMZz7DwIED6dWrF3PnzuUHP/gB559/PuPHj2fx4sUZaxs0aBA333wz5513HldffXWLD50777yTWbNmcdFFF7Fv377m4Zdeeilr165l/PjxPPPMM+1cO+np6pUSKa+s3s1tT73DZePO4JFvpLyQn2Swbt06Pv7xjxe6jIxmzJhB//79ufPOO3Oex9GjR+nfvz/79+9nwoQJLFq0iGHDhnVglfmR6m+kq1eKiKRw1VVXcejQIU6cOME999zTJUI+Fwp6EemSZsyY0e55NPXLZ+vLX/4yW7dubTHs/vvvb7XDt1go6EWkFXfXFSzb8Pzzzxds2bl0t2tnrIi00KdPH/bv359ToEh+Nd14pE+fPllNpy16EWmhrKyMyspKqqqqCl2KpNB0K8FsKOglkrQxmruePXtmdZs6KX7qupGIUb+ySDIFvUiO/uGZFfzdY0sKXYZIRuq6EcnR8+/uLHQJIqFoi14khPUfHGbU9JdZ/v7BQpcikjUFvUgIf9oQPwLl1TUfsPdI21cfFCk2CnqRLKzYcYgJP/kjzy6vLHQpIqEp6EVSOFJbT0NjrNXw9bvjt9V7e+v+ds1/ZeUhauuL+36mEh0Keun2rnroTSbd/3qLYefNeI07nlnRqu3h2vbfBGJ3dQ1f+uUi7np+VbvnJRKGgl66hdr6Rr7x+Nts2tP6Jg+rdx6m8mD87kX/tWgro6bH7zz00srdaee3cNO+tOOSuTt3Pb+Kim0HADgSfFisqqwG4IPqWo7Vtf8DRCQdBb10C4s27+PNTfu478W1bbZ7pmJHqPntqk69Q7b6eD1fm72YyoPHm4e5w2/f3s7XZqe+Nd3Ef/4j/+s/M9/YQiRXCnqJpOQrIHzz15lvZLPzUE27r9g4b+UuKrYdZNaC91rV0tasN6T4piHSURT0EinZ5HRtfSPXzjm5lf33TyzNebmrd1bz2JtbWgw70RAjFvPmq0Aml9bW5XgaGmP88LmVXDdnCUdq63OuSwQU9NKNLdhQxZItB5qfV9fkHqhXPbSQf3p5XfPzxpjz0X/8PT9+eW3CFn086sN8Fr20cje/W7qDt7bs51shvo2ItCVU0JvZ5Wa2wcw2m9n0FOPNzH4RjF9pZhckjPsHM1tjZqvN7Hdmlt2FlEWykOmqld7GdnTVkbqUw/ccruWNDXuzqqO+Mb6c37y9vbmmsF826htjnEg4tPPtrQfaaC2SWcagN7NS4GFgMjAWuM7MxiY1mwyMCX5uAWYF044AvgOUu/u5QClwbYdVL5KGAcfqGth7OLuzWFOF8VdmLW6x5d+mINXrE4I61tR1EyLpt+8/zpi7f89v394ebnkiIYTZop8AbHb3Le5+AngamJLUZgrwpMctAQaZ2fBgXA+gr5n1APoBuzqodpG0Yg7jfvQqE376x6ymWxucENVk7+Ha5kMvw7jnhTUAzPtL8DZP+AJhwcdIW186NgY7ZVfsOBR6mSKZhAn6EUDiMWeVwbCMbdx9J/AgsB3YDVS7+2upFmJmt5jZMjNbpjvbSK6atpr/sG5P87BH/9xyJ+nOQzXc/rt3qWvIfGZqth8UyRw/2Z3UjgN6ak7oLFrJXZigT/X2TN4oSdnGzD5EfGt/NPBh4BQz+3qqhbj7HHcvd/fyoUOHhihLJJyfzF/X4vmPXljDi3/ZxZ83hj/pqS1/TPhQSeZ+cr9A0z9Jurw/VtdAxfupu4iufnhROyqU7i5M0FcCIxOel9G6+yVdm78Btrp7lbvXA88BF+VerkjxyXSMftMWfV1DjB/MXZm23ff+7woe+dOWlON0nL20R5igrwDGmNloM+tFfGfqvKQ284Drg6NvJhLvotlNvMtmopn1s/ixZZ8D1iHSTTgnd8YCPLMs/Zm36z9QmEt+ZLzDlLs3mNk04FXiR8084e5rzOy2YPxsYD5wBbAZOA7cGIx728zmAu8ADcC7wJx8vBCRYpVu56snHQuqu91KvoS6laC7zyce5onDZic8dmBqmml/BPyoHTWKhFaMYZl8bH/iYZax2MmRJe28/IJIOjozVrqVTCdUdfzyvM3jKe9/Zf3JJ8p5yRMFvURKJ+d4Rsl99IlONMZ4JOHQT23RS74o6EXyyL314Z1NuX/oeMtr6yjmJV8U9NJtPftO59z3dW7I+8tu2ns0z5VId6Wgl0jJtFXc2X30qaiHRjqbgl6ki0g8QkckGwp66Va68tb08Xpd70Zyo6CXbmdd0hUqC0Yb6N3O00u38/M/bOz05SropVtxh6N1DQWtoemmJNlKPpNWup7pz63i53/Y1OnLVdBLpLT35t6d4afB4ZZHsvzAeW1N+qtkirRFQS/SyVbtrM5puode7/wtQYkGBb1ESlfo3si1xOJ/ZVKsFPQiIhGnoJduxYtgu7gL7EaQiFHQS6R0hZ2xIp1NQS/dimFU19RnbphHyRczE8k3Bb10K5uruu6Fw7rAfmYpUgp66VZqTugyAtL9KOhFuohi2JEsXZOCXrqVQl/+QKQQFPQSKVE+5mbHgZpClyBdlIJeIiXqnRtrduV2+QTp3hT0Il3IzoPaqpfsKehFRCJOQS/ShUS9a0ryQ0EvkRLlnbEiuVLQi4hEnIJeRCTiFPQiIhGnoBcRiTgFvUSKLkcv0pqCXiIl6pfyjfrrk/wIFfRmdrmZbTCzzWY2PcV4M7NfBONXmtkFCeMGmdlcM1tvZuvM7MKOfAEiItK2jEFvZqXAw8BkYCxwnZmNTWo2GRgT/NwCzEoY9x/AK+7+MeB8YF0H1C0iIiGF2aKfAGx29y3ufgJ4GpiS1GYK8KTHLQEGmdlwMxsAfBZ4HMDdT7j7oY4rX0REMgkT9COAHQnPK4NhYdqcDVQB/2Vm75rZY2Z2SjvqFWmTdsaKtBYm6FP96yTvEkrXpgdwATDL3T8JHANa9fEDmNktZrbMzJZVVVWFKEtERMIIE/SVwMiE52XArpBtKoFKd387GD6XePC34u5z3L3c3cuHDh0apnYREQkhTNBXAGPMbLSZ9QKuBeYltZkHXB8cfTMRqHb33e7+AbDDzM4J2n0OWNtRxYuISGY9MjVw9wYzmwa8CpQCT7j7GjO7LRg/G5gPXAFsBo4DNybM4nbgN8GHxJakcSKSFR1IL9nLGPQA7j6feJgnDpud8NiBqWmmXQGU516iSHjaGSvSms6MlUjRmaMirSnoRboQfZBJLhT0IiIRp6AX6UK0D0JyoaCXSFEQirSmoBfpQtRHL7lQ0IuIRJyCXkQk4hT0Il2Iem4kFwp6EZGIU9CLdCE6qEhyoaAXEYk4Bb1IF6I+esmFgl5EJOIU9CIiEaegl0ixiO+u1JmxkgsFvYhIxCnoRboQXbRNcqGgFxGJOAW9SBeiPnrJhYJeRCTiFPQiIhGnoBfpQlznxkoOFPQiIhGnoBfpQqJ+Qpjkh4JeokU5KNKKgl6kC1EfveRCQS8iEnEKehGRiFPQi3QhOjNWcqGgFxGJOAW9SBeiq1dKLhT0IiIRFyrozexyM9tgZpvNbHqK8WZmvwjGrzSzC5LGl5rZu2b2UkcVLtIdqY9ecpEx6M2sFHgYmAyMBa4zs7FJzSYDY4KfW4BZSeO/C6xrd7UiIpK1MFv0E4DN7r7F3U8ATwNTktpMAZ70uCXAIDMbDmBmZcCVwGMdWLdISurCFmktTNCPAHYkPK8MhoVt83Pg+0CsrYWY2S1mtszMllVVVYUoS6T7Uc+N5CJM0KfaSEp+v6VsY2ZXAXvdfXmmhbj7HHcvd/fyoUOHhihLRETCCBP0lcDIhOdlwK6QbS4GvmRm24h3+fy1mT2Vc7UiIpK1MEFfAYwxs9Fm1gu4FpiX1GYecH1w9M1EoNrdd7v7D929zN1HBdO97u5f78gXINKdaB+E5KJHpgbu3mBm04BXgVLgCXdfY2a3BeNnA/OBK4DNwHHgxvyVLNJ9qY9ecpEx6AHcfT7xME8cNjvhsQNTM8xjAbAg6wpFRKRddGasiEjEKehFuhDXqbGSAwW9iEjEKehFRCJOQS+RYhG/jm/UX5/kh4JepAtRH73kQkEvIhJxCnqJFG3xirSmoBcRiTgFvUSKdlaKtKagFxGJOAW9iEjEKehFRCJOQS8iEnEKeokU7YoVaU1BL9KF6DQByYWCXkQk4hT0Eina4BVpTUEv0oXofDDJhYJeIiXqOag+esmFgl5EJOIU9CIiEaegF+lCXLubJQcKehGRiFPQS6ToqBSR1hT0Il2IRf64IskHBb1EStQPP1QfveRCQS8iEnEKehGRiFPQS6REfWds1LumJD8U9CIiEaegFxGJuFBBb2aXm9kGM9tsZtNTjDcz+0UwfqWZXRAMH2lmb5jZOjNbY2bf7egXICIibcsY9GZWCjwMTAbGAteZ2dikZpOBMcHPLcCsYHgD8L/d/ePARGBqimlFRCSPwmzRTwA2u/sWdz8BPA1MSWozBXjS45YAg8xsuLvvdvd3ANz9CLAOGNGB9YuISAZhgn4EsCPheSWtwzpjGzMbBXwSeDvrKkVC0pmjIq2FCfpU/znJB3m12cbM+gPPAne4++GUCzG7xcyWmdmyqqqqEGWJtBb1M0d1eKXkIkzQVwIjE56XAbvCtjGznsRD/jfu/ly6hbj7HHcvd/fyoUOHhqldRERCCBP0FcAYMxttZr2Aa4F5SW3mAdcHR99MBKrdfbeZGfA4sM7d/61DKxcRkVB6ZGrg7g1mNg14FSgFnnD3NWZ2WzB+NjAfuALYDBwHbgwmvxj4BrDKzFYEw+5y9/kd+ipERCStjEEPEATz/KRhsxMeOzA1xXQLif79mqWIaGesSGs6M1ZEJOIU9CIiEaegF+lCdHSl5EJBLyIScQp6iZSoX49eJBcKeokUnTkq0pqCXkQk4hT0IiIRp6AXEYk4Bb1EStR3xrp2QkgOFPQiIhGnoBcRiTgFvYhIxCnoRUQiTkEvkRLxfbEiOVHQS6TomBSR1hT0Il2IPsgkFwp6EZGIU9CLiEScgl4iRTtjRVpT0EtBxGLO4wu3cqyuodCliESegl4K4g/r9vDjl9by0/nrCl2KSOQp6CUvjtU18MrqD9KOr6lvBOBwrbboRfJNQS95cdfzq7jtqeWs/+BwoUuJFh1fKTlQ0EteVB6sAeBohi32Dr/srvbGSifaUnWUp5duL3QZGfUodAESTU15G+vsLVBt8UonmvLwIo7UNnDNp0diRXwzBG3RS16UBG/6TFvsmf45jtTWM2r6y8z583sdVptIRzkSfGNt7PQtmux0u6DfuOcIN/2qgrqGxkKXEm1Bfmd6/2f6IDhw7AQATy0p/q/H0n3VNyroi8pdz63i9fV7+cuO6kKXEmklQdB7O/tSmr4ZxHQLPSlCpcEb/URjrMCVtK3bBb2Co3MYTV03Gdpl6LppGq0/lxSj0uAN2qCgLy7W3KWg5MinkuCdlWk1Z+q6afpgDt0HWrz7wzpEe78hScdqep83hHh/Hqmtz3M16XW7oG/6qqWcz6+mLfp0H6hhj1DQB7MUs6Yt+voQW/R/9bMFea4mvVBBb2aXm9kGM9tsZtNTjDcz+0UwfqWZXRB22s6W9Rai5KS5yyXN+LDHzzf9mfTnkmJUUtIU9JnfoE0HFhRCxqA3s1LgYWAyMBa4zszGJjWbDIwJfm4BZmUxbafSFmLnsA7aFxILEr7DT6zqovYdLVxYSGtNPQRR6KOfAGx29y3ufgJ4GpiS1GYK8KTHLQEGmdnwkNPmxZHaesbe+wpvrN/bPKyhMdZhXTfPv1vJR+6aT21924dpfvGhhcx8cW37FtYFNR91086um6YPirAfGCVFfNJKR9jwwZFClyAJmjZEmrboJ93/Oo8v3Nqq3Zubqjq1rmQW4oSWrwKXu/u3guffAD7j7tMS2rwE/Iu7Lwye/xH4ATAq07SplJeX+7Jly7J6Ie7Ol/9zMb17lFBixsHjJ1gf/FNcePZglm8/yImGk5+6o4ecwhkDeifNI828UwxbuvUAAGcN7scZA/o0D7ek9k3tLjx7cFavpyM5zpItBzjztH4MG9gnXqDFwzgWg8O19fTrVUqP0vjnflNUmsXXSV1DjBMNMfr37tE8rLqmng17jjDm9P4M6d+71TLf2rIfiK/nYQnrp8mmvUfZd7QOgPNGDKS6pp5hA/oQc2d3dS0D+vZkYN8eVGw72NzN9ulRH6LEDCf+9455/AMgFh9AzOFoXQNb9x0D4PyygQzp35sTjTFGntaPwzX19OlZyoINe/n6xLOoa4jxuY+dzjMVOzhS28Ca3dUMH9iXK84dRvmo07jr+VWsrKxmSP9efOt/nk19Q4wFG6s4tU8PFr+3nxMNMf62vIwJowdzSq9Slr9/kIvHDKFnSQlvbq7iWF0DTy3ZzlPf/AyL39vH5r1HeW3tHgBu/auz2XfkBM++U8kXxp7B8IF9qDpax4RRp/HKmg8YMagfM6eMo64hRsW2A3x82AA++7M3OPO0fnx4UOv1mc17N92IdDt6s513ukxJ3z67+aeaIPt5Z/la0wxfuzt+LadPlA2kf+/4+wLgoo+0/H/feaiG9/cfb36ePL7JwL49mfX1T6VeWAZmttzdy1ONC3MJhFSbSMkvO12bMNPGZ2B2C/FuH84888wQZbVUWx/j7CGnUHmwhkZ3BvTpCcCIQX1pjDmn9u7B/oYTfPSM/mzcc5Sh/Xun7PdNVbBxssunybABffjgcC3DB8b/6ZreCJ6mfTHsE9h+4DjDB/bBSuJ93rFY/KiB3j1Lqa2PMaBHSYvX4bH46+jTs4TqmnoG9O2Be3yapm9GB4/X86F+vVotq2k9D+nfK+VrP2twP/YdreOswf0Y3L8XNfWNmEGv0hIG9etJfWOMWOxkaJR9qC+lJRZfvkFJSfwD3Sy+FV8S/D5jQB/OLxvIfV86l4H9eoZaL+WjTks5fN60Sa2G3f65MWnnM/m84c2PJ40ZAsA/XX1ei+fJ/vVvz2817IaLRzc/PqU3XDZuGADXX3gW63cfIZaqlyDNF5mU72dLPcJSfMFP9wUp7fAUM872S1aqb3vpZpFq3unbph6Tdh2FaN2j1FhZWU3vHiXNO2QTHzc5/dTefO5jZ/D5sWfw73/YmHbnbb5OvAoT9JXAyITnZcCukG16hZgWAHefA8yB+BZ9iLpa6NurlH+7Zny2k4l0GTOnnFvoEqQDXPiRCzt9mWH66CuAMWY22sx6AdcC85LazAOuD46+mQhUu/vukNOKiEgeZdyid/cGM5sGvAqUAk+4+xozuy0YPxuYD1wBbAaOAze2NW1eXomIiKSUcWdsIeSyM1ZEpDtra2dstzszVkSku1HQi4hEnIJeRCTiFPQiIhGnoBcRibiiPOrGzKqA93OcfAiwrwPL6QjFWBOormwVY13FWBOormx0VE1nufvQVCOKMujbw8yWpTvEqFCKsSZQXdkqxrqKsSZQXdnojJrUdSMiEnEKehGRiIti0M8pdAEpFGNNoLqyVYx1FWNNoLqykfeaItdHLyIiLUVxi15ERBJEJugLfRNyM9tmZqvMbIWZLQuGnWZm/21mm4LfH0po/8Og1g1mdlkH1vGEme01s9UJw7Kuw8w+FbyezcGN33O+R1+ammaY2c5gfa0wsys6s6ZgfiPN7A0zW2dma8zsu8Hwgq2vNmoq6Poysz5mttTM/hLUdV8wvNDvrXR1FcP7q9TM3rX4HfgKu67cvcv/EL8E8nvA2cRvdvIXYGwn17ANGJI07AFgevB4OnB/8HhsUGNvYHRQe2kH1fFZ4AJgdXvqAJYCFxK/pc7vgckdXNMM4M4UbTulpmB+w4ELgsenAhuD5RdsfbVRU0HXVzCP/sHjnsDbwMQieG+lq6sY3l/fA34LvFTo/8OobNEX7CbkGUwBfh08/jVwdcLwp929zt23Er+O/4SOWKC7/xk40J46LH5j9wHu/pbH321PJkzTUTWl0yk1BXXtdvd3gsdHgHXACAq4vtqoKZ3O+hu6ux8NnvYMfpzCv7fS1ZVOp9RlZmXAlcBjScsuyLqKStCPAHYkPK+k7X+OfHDgNTNbbvH73wKc4fE7bRH8Pj0Y3tn1ZlvHiOBxvuubZmYrLd610/Q1tiA1mdko4JPEtwiLYn0l1QQFXl9BV8QKYC/w3+5eFOsqTV1Q2PX1c+D7QOLNYQu2rqIS9KFvQp5HF7v7BcBkYKqZfbaNtsVQL3TATd3bYRbwEWA8sBv410LVZGb9gWeBO9z9cFtNO6u2FDUVfH25e6O7jyd+7+cJZtbWTWwLXVfB1peZXQXsdfflYSfJd01RCfowNzDPK3ffFfzeCzxPvCtmT/D1i+D33qB5Z9ebbR2VweO81efue4J/0BjwKCe7rjq1JjPrSTxQf+PuzwWDC7q+UtVULOsrqOUQsAC4nCJ6byXWVeD1dTHwJTPbRrwb+a/N7CkKua7as7OhWH6I3/t2C/EdGU07Y8d14vJPAU5NeLyY+D/Bz2i58+WB4PE4Wu582UIH7YwN5j+Kljs+s66D+I3dJ3JyJ9AVHVzT8ITH/0C8j7KzazLi/Z4/TxpesPXVRk0FXV/AUGBQ8Lgv8CZwVaHfW23UVfD3VzDPSzi5M7Zw76v2vpBi+SF+c/KNxPdY393Jyz47+EP9BVjTtHxgMPBHYFPw+7SEae4Oat1AO/fuJ9XyO+JfVeuJbxF8M5c6gHJgdTDulwQn13VgTf8HWAWsBOYl/WPmvaZgfpOIfxVeCawIfq4o5Ppqo6aCri/gE8C7wfJXA/fm+h7vpLoK/v4K5nkJJ4O+YOtKZ8aKiERcVProRUQkDQW9iEjEKehFRCJOQS8iEnEKehGRiFPQi4hEnIJeJCQzu6TpkrMdOM+7Eh6PsoRLOYt0FAW9RJqZlRa6hgzuytxEpH0U9NJlBVvA683s18FVCueaWT+L3wTmXjNbCHzNzL5gZm+Z2Ttm9v+CC4ZhZlcE0y8MburQdIOIU4IrHlYEN44IdclrM5tgZouDaRab2TnB8BvM7JcJ7V4Kvh38C9A3uDHGb4LRpWb2aHATjdfMrG+HrjTplhT00tWdA8xx908Ah4FvB8Nr3X0S8AfgH4G/8fjVRZcB3zOzPsAjxE83n0T8milN7gZed/dPA5cCPzOzU0LUsh74rLt/ErgX+Glbjd19OlDj7uPd/e+CwWOAh919HHAI+EqI5Yq0qUehCxBppx3uvih4/BTwneDxM8HvicTv4LMouAtbL+At4GPAFo/f6AHi1+Npuo/AF4hfffDO4Hkf4MwQtQwEfm1mY4hfr6ZnDq9nq7uvCB4vJ34xOJF2UdBLV5d8saam58eC30b8ZhTXJTYys0+2MU8DvuLuG5KmOSNDLT8G3nD3Lwc3DVkQDG+g5bfnPm3Moy7hcSPxKzKKtIu6bqSrO9PMLgweXwcsTBq/BLjYzP4HQNCH/1Hi3SxnB4EMcE3CNK8CtzfdiDnDh0KigcDO4PENCcO3AePNrMTMRtLytpH1wfXnRfJGQS9d3Trg781sJXAa8TsLNXP3KuKh+7ugzRLgY+5eQ7w//5Vgp+0eoDqY7MfEu11WBoc7/jhkLQ8A/2xmi4jfsL7JImAr8cvmPgi8kzBuTrCc3yCSJ7pMsXRZwdb4S+7e1i3t2pq+v7sfDbbcHwY2ufu/d2SNIsVAW/TSnd0c3FR6DfFul0cKW45IfmiLXiRLZnYj8N2kwYvcfWoh6hHJREEvIhJx6roREYk4Bb2ISMQp6EVEIk5BLyIScQp6EZGI+/94knzD2NovsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.pregel_auth'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7ee69036",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pregel_hub'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.pregel_hub'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
